Face Generation

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data

You'll be using two datasets in this project:

  • MNIST
  • CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

In [1]:
data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Found mnist Data
Found celeba Data

Explore the Data

MNIST

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

In [2]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
Out[2]:
<matplotlib.image.AxesImage at 0x7f73526d9128>

CelebA

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

In [3]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Out[3]:
<matplotlib.image.AxesImage at 0x7f7352609978>

Preprocess the Data

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network

You'll build the components necessary to build a GANs by implementing the following functions below:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

Check the Version of TensorFlow and Access to GPU

This will check to make sure you have the correct version of TensorFlow and access to a GPU

In [4]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
TensorFlow Version: 1.0.0
Default GPU Device: /gpu:0

Input

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

  • Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
  • Z input placeholder with rank 2 using z_dim.
  • Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

In [5]:
import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    real_dim = (image_width, image_height, image_channels)
    inputs_real = tf.placeholder(tf.float32, (None, *real_dim), name='input_real')
    inputs_z = tf.placeholder(tf.float32, (None, z_dim), name='input_z')
    lr = tf.placeholder(tf.float32, None, name='learning_rate')

    return inputs_real, inputs_z, lr


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Tests Passed

Discriminator

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).

In [6]:
def discriminator(images, reuse=False):
    """
    Create the discriminator network
    :param images: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    with tf.variable_scope('discriminator', reuse=reuse):
        alpha=0.2
        # Input layer is 28x28x3
        x1 = tf.layers.conv2d(images, 64, 5, strides=2, padding='same')
        relu1 = tf.maximum(alpha * x1, x1)
        # 14x14x64
        
        x2 = tf.layers.conv2d(relu1, 128, 5, strides=2, padding='same')
        bn2 = tf.layers.batch_normalization(x2, training=True)
        relu2 = tf.maximum(alpha * bn2, bn2)
        # 7x7x128
        
        x3 = tf.layers.conv2d(relu2, 256, 5, strides=2, padding='same')
        bn3 = tf.layers.batch_normalization(x3, training=True)
        relu3 = tf.maximum(alpha * bn3, bn3)
        # 4x4x256

        # Flatten it
        flat = tf.reshape(relu3, (-1, 4*4*256))
        logits = tf.layers.dense(flat, 1)
        out = tf.sigmoid(logits)
        
        return out, logits


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Tests Passed

Generator

Implement generator to generate an image using z. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

In [7]:
def generator(z, out_channel_dim, is_train=True):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    with tf.variable_scope('generator', reuse= not is_train):
        alpha=0.2
        # First fully connected layer
        x1 = tf.layers.dense(z, 7*7*512)
        # Reshape it to start the convolutional stack
        x1 = tf.reshape(x1, (-1, 7, 7, 512))
        x1 = tf.layers.batch_normalization(x1, training=is_train)
        x1 = tf.maximum(alpha * x1, x1)
        # 7x7x512 now
        
        x2 = tf.layers.conv2d_transpose(x1, 256, 5, strides=2, padding='same')
        x2 = tf.layers.batch_normalization(x2, training=is_train)
        x2 = tf.maximum(alpha * x2, x2)
        # 14x14x256 now
        
        # Output layer
        logits = tf.layers.conv2d_transpose(x2, out_channel_dim, 5, strides=2, padding='same')
        # 28x28x3 now
        
        out = tf.tanh(logits)
        
        return out


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Tests Passed

Loss

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)
In [8]:
def model_loss(input_real, input_z, out_channel_dim):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    g_model = generator(input_z, out_channel_dim,)
    d_model_real, d_logits_real = discriminator(input_real)
    d_model_fake, d_logits_fake = discriminator(g_model, reuse=True)

    d_loss_real = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=tf.ones_like(d_model_real * 0.9)))
    d_loss_fake = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.zeros_like(d_model_fake)))
    g_loss = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.ones_like(d_model_fake)))

    d_loss = d_loss_real + d_loss_fake

    return d_loss, g_loss


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tests Passed

Optimization

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

In [9]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    # Get weights and bias to update
    t_vars = tf.trainable_variables()
    d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
    g_vars = [var for var in t_vars if var.name.startswith('generator')]

    # Optimize
    with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
        d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
        g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)

    return d_train_opt, g_train_opt


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed

Neural Network Training

Show Output

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

In [10]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train

Implement train to build and train the GANs. Use the following functions you implemented:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

In [11]:
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    samples, losses = [], []
    steps = 0
    
    num_imgs, img_width, img_height, img_channels = data_shape
    real_size = [img_width, img_height, img_channels]
    
    tf.reset_default_graph()
        
    input_real, input_z, lr = model_inputs(img_width, img_height, img_channels, z_dim)

    d_loss, g_loss = model_loss(input_real, input_z, real_size[2])

    d_opt, g_opt = model_opt(d_loss, g_loss, learning_rate, beta1)
    
    
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch_i in range(epoch_count):
            for batch_images in get_batches(batch_size):
                steps += 1
                batch_images = batch_images * 2

                # Sample random noise for G
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))

                # Run optimizers
                _ = sess.run(d_opt, feed_dict={input_real: batch_images, input_z: batch_z})
                _ = sess.run(g_opt, feed_dict={input_z: batch_z, input_real: batch_images})

                if steps % 100 == 0:
                    # At the end of each epoch, get the losses and print them out
                    train_loss_d = d_loss.eval({input_z: batch_z, input_real: batch_images})
                    train_loss_g = g_loss.eval({input_z: batch_z})

                    print("Epoch {}/{}...".format(epoch_i+1, epochs),
                          "Discriminator Loss: {:.4f}...".format(train_loss_d),
                          "Generator Loss: {:.4f}".format(train_loss_g))
                    # Save losses to view after training
                    losses.append((train_loss_d, train_loss_g))
                    show_generator_output(sess, 25, input_z, img_channels, data_image_mode)
    

MNIST

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

In [12]:
batch_size = 32
z_dim = 100
learning_rate = 0.0002
beta1 = 0.4


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)
Epoch 1/2... Discriminator Loss: 1.2154... Generator Loss: 0.9704
Epoch 1/2... Discriminator Loss: 1.3517... Generator Loss: 0.4052
Epoch 1/2... Discriminator Loss: 0.5950... Generator Loss: 1.5685
Epoch 1/2... Discriminator Loss: 0.7069... Generator Loss: 1.4526
Epoch 1/2... Discriminator Loss: 0.9859... Generator Loss: 0.9204
Epoch 1/2... Discriminator Loss: 0.7850... Generator Loss: 1.5685
Epoch 1/2... Discriminator Loss: 0.8454... Generator Loss: 0.8880
Epoch 1/2... Discriminator Loss: 0.7891... Generator Loss: 0.8205
Epoch 1/2... Discriminator Loss: 0.7490... Generator Loss: 0.9300
Epoch 1/2... Discriminator Loss: 0.8081... Generator Loss: 1.0012
Epoch 1/2... Discriminator Loss: 0.7009... Generator Loss: 1.9606
Epoch 1/2... Discriminator Loss: 0.6992... Generator Loss: 1.2775
Epoch 1/2... Discriminator Loss: 0.6121... Generator Loss: 1.1047
Epoch 1/2... Discriminator Loss: 0.8198... Generator Loss: 0.9934
Epoch 1/2... Discriminator Loss: 2.1370... Generator Loss: 0.1644
Epoch 1/2... Discriminator Loss: 0.7978... Generator Loss: 2.0592
Epoch 1/2... Discriminator Loss: 0.6647... Generator Loss: 0.9492
Epoch 1/2... Discriminator Loss: 0.6629... Generator Loss: 1.0650
Epoch 2/2... Discriminator Loss: 0.7737... Generator Loss: 0.8670
Epoch 2/2... Discriminator Loss: 0.8007... Generator Loss: 0.8469
Epoch 2/2... Discriminator Loss: 1.0345... Generator Loss: 0.5690
Epoch 2/2... Discriminator Loss: 0.7862... Generator Loss: 0.7900
Epoch 2/2... Discriminator Loss: 0.9448... Generator Loss: 0.6946
Epoch 2/2... Discriminator Loss: 1.0767... Generator Loss: 0.5946
Epoch 2/2... Discriminator Loss: 1.3229... Generator Loss: 0.4687
Epoch 2/2... Discriminator Loss: 0.5791... Generator Loss: 1.1461
Epoch 2/2... Discriminator Loss: 0.6258... Generator Loss: 0.9621
Epoch 2/2... Discriminator Loss: 0.4533... Generator Loss: 1.3776
Epoch 2/2... Discriminator Loss: 0.5886... Generator Loss: 1.2290
Epoch 2/2... Discriminator Loss: 1.1579... Generator Loss: 0.6154
Epoch 2/2... Discriminator Loss: 0.6667... Generator Loss: 0.9914
Epoch 2/2... Discriminator Loss: 0.4212... Generator Loss: 1.3505
Epoch 2/2... Discriminator Loss: 0.7213... Generator Loss: 0.8222
Epoch 2/2... Discriminator Loss: 0.3889... Generator Loss: 1.3609
Epoch 2/2... Discriminator Loss: 0.4163... Generator Loss: 1.4296
Epoch 2/2... Discriminator Loss: 0.6668... Generator Loss: 0.8849
Epoch 2/2... Discriminator Loss: 0.6001... Generator Loss: 1.1311
---------------------------------------------------------------------------
IndexError                                Traceback (most recent call last)
<ipython-input-12-bada83a74b85> in <module>()
     13 with tf.Graph().as_default():
     14     train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
---> 15           mnist_dataset.shape, mnist_dataset.image_mode)

/usr/lib/python3.5/contextlib.py in __exit__(self, type, value, traceback)
     64         if type is None:
     65             try:
---> 66                 next(self.gen)
     67             except StopIteration:
     68                 return

/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py in get_controller(self, default)
   3682     finally:
   3683       if self._enforce_nesting:
-> 3684         if self.stack[-1] is not default:
   3685           raise AssertionError(
   3686               "Nesting violated for default stack of %s objects"

IndexError: list index out of range

CelebA

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

In [13]:
batch_size = 16
z_dim = 100
learning_rate =  0.0002
beta1 = 0.4


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)
Epoch 1/1... Discriminator Loss: 1.1830... Generator Loss: 0.6582
Epoch 1/1... Discriminator Loss: 0.9516... Generator Loss: 1.3173
Epoch 1/1... Discriminator Loss: 1.8023... Generator Loss: 0.3103
Epoch 1/1... Discriminator Loss: 3.8032... Generator Loss: 0.0265
Epoch 1/1... Discriminator Loss: 0.0858... Generator Loss: 3.0462
Epoch 1/1... Discriminator Loss: 0.6293... Generator Loss: 8.7145
Epoch 1/1... Discriminator Loss: 0.4037... Generator Loss: 1.6327
Epoch 1/1... Discriminator Loss: 0.0762... Generator Loss: 7.0147
Epoch 1/1... Discriminator Loss: 0.0941... Generator Loss: 9.0158
Epoch 1/1... Discriminator Loss: 0.8723... Generator Loss: 0.6754
Epoch 1/1... Discriminator Loss: 0.0665... Generator Loss: 4.2029
Epoch 1/1... Discriminator Loss: 0.0650... Generator Loss: 4.4621
Epoch 1/1... Discriminator Loss: 0.9085... Generator Loss: 0.6806
Epoch 1/1... Discriminator Loss: 0.0871... Generator Loss: 8.6965
Epoch 1/1... Discriminator Loss: 0.0098... Generator Loss: 6.5895
Epoch 1/1... Discriminator Loss: 2.2130... Generator Loss: 0.1930
Epoch 1/1... Discriminator Loss: 0.0464... Generator Loss: 4.7012
Epoch 1/1... Discriminator Loss: 0.0545... Generator Loss: 6.8799
Epoch 1/1... Discriminator Loss: 0.0320... Generator Loss: 6.8000
Epoch 1/1... Discriminator Loss: 0.2631... Generator Loss: 1.9911
Epoch 1/1... Discriminator Loss: 0.2449... Generator Loss: 1.8871
Epoch 1/1... Discriminator Loss: 0.0199... Generator Loss: 6.6993
Epoch 1/1... Discriminator Loss: 1.3495... Generator Loss: 0.3641
Epoch 1/1... Discriminator Loss: 0.0526... Generator Loss: 3.4531
Epoch 1/1... Discriminator Loss: 0.0089... Generator Loss: 10.9945
Epoch 1/1... Discriminator Loss: 0.0618... Generator Loss: 4.1530
Epoch 1/1... Discriminator Loss: 0.0120... Generator Loss: 6.6739
Epoch 1/1... Discriminator Loss: 0.0682... Generator Loss: 4.9467
Epoch 1/1... Discriminator Loss: 0.0088... Generator Loss: 10.2763
Epoch 1/1... Discriminator Loss: 0.3234... Generator Loss: 1.5852
Epoch 1/1... Discriminator Loss: 0.0338... Generator Loss: 4.0422
Epoch 1/1... Discriminator Loss: 0.1256... Generator Loss: 4.7396
Epoch 1/1... Discriminator Loss: 0.0059... Generator Loss: 9.3607
Epoch 1/1... Discriminator Loss: 1.1024... Generator Loss: 9.1635
Epoch 1/1... Discriminator Loss: 0.0173... Generator Loss: 6.0861
Epoch 1/1... Discriminator Loss: 0.0700... Generator Loss: 3.8146
Epoch 1/1... Discriminator Loss: 0.0853... Generator Loss: 5.6693
Epoch 1/1... Discriminator Loss: 0.0310... Generator Loss: 5.6020
Epoch 1/1... Discriminator Loss: 0.4337... Generator Loss: 1.6041
Epoch 1/1... Discriminator Loss: 0.0476... Generator Loss: 3.8139
Epoch 1/1... Discriminator Loss: 0.0194... Generator Loss: 8.1957
Epoch 1/1... Discriminator Loss: 0.2750... Generator Loss: 1.5555
Epoch 1/1... Discriminator Loss: 0.0057... Generator Loss: 7.3647
Epoch 1/1... Discriminator Loss: 0.0189... Generator Loss: 7.1752
Epoch 1/1... Discriminator Loss: 0.0043... Generator Loss: 8.3839
Epoch 1/1... Discriminator Loss: 0.9700... Generator Loss: 0.6042
Epoch 1/1... Discriminator Loss: 0.0316... Generator Loss: 4.3776
Epoch 1/1... Discriminator Loss: 0.0169... Generator Loss: 5.5063
Epoch 1/1... Discriminator Loss: 1.7325... Generator Loss: 0.4331
Epoch 1/1... Discriminator Loss: 0.0138... Generator Loss: 7.1412
Epoch 1/1... Discriminator Loss: 0.0141... Generator Loss: 10.3343
Epoch 1/1... Discriminator Loss: 1.0826... Generator Loss: 3.3892
Epoch 1/1... Discriminator Loss: 0.4277... Generator Loss: 1.6895
Epoch 1/1... Discriminator Loss: 0.0859... Generator Loss: 5.8352
Epoch 1/1... Discriminator Loss: 0.0451... Generator Loss: 5.5974
Epoch 1/1... Discriminator Loss: 0.1169... Generator Loss: 3.7746
Epoch 1/1... Discriminator Loss: 0.0252... Generator Loss: 6.9900
Epoch 1/1... Discriminator Loss: 0.0402... Generator Loss: 5.7631
Epoch 1/1... Discriminator Loss: 0.2122... Generator Loss: 2.9627
Epoch 1/1... Discriminator Loss: 0.0297... Generator Loss: 4.4816
Epoch 1/1... Discriminator Loss: 0.3091... Generator Loss: 1.8340
Epoch 1/1... Discriminator Loss: 0.0986... Generator Loss: 2.9590
Epoch 1/1... Discriminator Loss: 0.6337... Generator Loss: 1.0925
Epoch 1/1... Discriminator Loss: 0.0153... Generator Loss: 7.8427
Epoch 1/1... Discriminator Loss: 0.4779... Generator Loss: 1.3098
Epoch 1/1... Discriminator Loss: 0.9726... Generator Loss: 4.7168
Epoch 1/1... Discriminator Loss: 0.0231... Generator Loss: 4.2866
Epoch 1/1... Discriminator Loss: 0.0445... Generator Loss: 9.0846
Epoch 1/1... Discriminator Loss: 0.0345... Generator Loss: 3.9859
Epoch 1/1... Discriminator Loss: 0.0176... Generator Loss: 5.9968
Epoch 1/1... Discriminator Loss: 0.7320... Generator Loss: 1.3110
Epoch 1/1... Discriminator Loss: 0.4149... Generator Loss: 3.0509
Epoch 1/1... Discriminator Loss: 0.0072... Generator Loss: 6.3854
Epoch 1/1... Discriminator Loss: 0.0284... Generator Loss: 5.1508
Epoch 1/1... Discriminator Loss: 0.1184... Generator Loss: 6.9858
Epoch 1/1... Discriminator Loss: 0.0887... Generator Loss: 3.2628
Epoch 1/1... Discriminator Loss: 0.4020... Generator Loss: 1.4490
Epoch 1/1... Discriminator Loss: 0.0441... Generator Loss: 3.7988
Epoch 1/1... Discriminator Loss: 2.0740... Generator Loss: 4.6725
Epoch 1/1... Discriminator Loss: 0.0447... Generator Loss: 4.9571
Epoch 1/1... Discriminator Loss: 0.0219... Generator Loss: 7.1337
Epoch 1/1... Discriminator Loss: 0.0593... Generator Loss: 3.6979
Epoch 1/1... Discriminator Loss: 0.0334... Generator Loss: 8.5397
Epoch 1/1... Discriminator Loss: 0.0164... Generator Loss: 6.7375
Epoch 1/1... Discriminator Loss: 0.0322... Generator Loss: 8.4452
Epoch 1/1... Discriminator Loss: 0.6049... Generator Loss: 1.0068
Epoch 1/1... Discriminator Loss: 0.0240... Generator Loss: 4.9569
Epoch 1/1... Discriminator Loss: 0.1927... Generator Loss: 6.0730
Epoch 1/1... Discriminator Loss: 0.0489... Generator Loss: 9.2035
Epoch 1/1... Discriminator Loss: 0.3785... Generator Loss: 2.0931
Epoch 1/1... Discriminator Loss: 1.0569... Generator Loss: 0.5725
Epoch 1/1... Discriminator Loss: 0.2969... Generator Loss: 1.7567
Epoch 1/1... Discriminator Loss: 1.2250... Generator Loss: 0.4819
Epoch 1/1... Discriminator Loss: 1.8085... Generator Loss: 0.2446
Epoch 1/1... Discriminator Loss: 0.2785... Generator Loss: 1.6934
Epoch 1/1... Discriminator Loss: 0.0944... Generator Loss: 3.1413
Epoch 1/1... Discriminator Loss: 0.6738... Generator Loss: 1.1657
Epoch 1/1... Discriminator Loss: 0.3069... Generator Loss: 1.8500
Epoch 1/1... Discriminator Loss: 0.0698... Generator Loss: 3.4071
Epoch 1/1... Discriminator Loss: 0.0077... Generator Loss: 6.4863
Epoch 1/1... Discriminator Loss: 0.4896... Generator Loss: 1.8758
Epoch 1/1... Discriminator Loss: 0.0550... Generator Loss: 8.7826
Epoch 1/1... Discriminator Loss: 0.0797... Generator Loss: 3.6052
Epoch 1/1... Discriminator Loss: 0.0787... Generator Loss: 5.1907
Epoch 1/1... Discriminator Loss: 0.0847... Generator Loss: 3.0918
Epoch 1/1... Discriminator Loss: 1.3460... Generator Loss: 0.5449
Epoch 1/1... Discriminator Loss: 0.3814... Generator Loss: 2.8953
Epoch 1/1... Discriminator Loss: 0.7592... Generator Loss: 1.0318
Epoch 1/1... Discriminator Loss: 1.4729... Generator Loss: 0.3321
Epoch 1/1... Discriminator Loss: 0.4689... Generator Loss: 1.3245
Epoch 1/1... Discriminator Loss: 0.0108... Generator Loss: 5.6089
Epoch 1/1... Discriminator Loss: 1.2433... Generator Loss: 0.4767
Epoch 1/1... Discriminator Loss: 0.1048... Generator Loss: 2.8595
Epoch 1/1... Discriminator Loss: 0.0279... Generator Loss: 7.2869
Epoch 1/1... Discriminator Loss: 0.1007... Generator Loss: 3.0220
Epoch 1/1... Discriminator Loss: 0.0350... Generator Loss: 4.2588
Epoch 1/1... Discriminator Loss: 0.5568... Generator Loss: 3.6122
Epoch 1/1... Discriminator Loss: 0.0496... Generator Loss: 5.4704
Epoch 1/1... Discriminator Loss: 0.0574... Generator Loss: 3.6058
Epoch 1/1... Discriminator Loss: 0.0841... Generator Loss: 3.9737
Epoch 1/1... Discriminator Loss: 0.2218... Generator Loss: 1.9192
Epoch 1/1... Discriminator Loss: 0.0472... Generator Loss: 5.8837
Epoch 1/1... Discriminator Loss: 0.0526... Generator Loss: 3.9179
Epoch 1/1... Discriminator Loss: 0.0710... Generator Loss: 3.4661
Epoch 1/1... Discriminator Loss: 0.2592... Generator Loss: 2.7657
Epoch 1/1... Discriminator Loss: 0.7015... Generator Loss: 0.9263
---------------------------------------------------------------------------
IndexError                                Traceback (most recent call last)
<ipython-input-13-990f2122e711> in <module>()
     13 with tf.Graph().as_default():
     14     train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
---> 15           celeba_dataset.shape, celeba_dataset.image_mode)

/usr/lib/python3.5/contextlib.py in __exit__(self, type, value, traceback)
     64         if type is None:
     65             try:
---> 66                 next(self.gen)
     67             except StopIteration:
     68                 return

/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py in get_controller(self, default)
   3682     finally:
   3683       if self._enforce_nesting:
-> 3684         if self.stack[-1] is not default:
   3685           raise AssertionError(
   3686               "Nesting violated for default stack of %s objects"

IndexError: list index out of range

Submitting This Project

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.